CoViMAPP (CoViD-19 Meta-Analysis of Plasma Proteins) is a meta-analysis resource of plasma proteins' alterations in COVID-19. CoViMAPP includes summary summary estimates obtained from published studies of plasma proteome profiling in COVID-19 by unbiased mass-spectrometry proteomics, identified through a systematic review by Babačić et al. (2023). Details can be found in the corresponding manuscript.
This resource is divided into several sections:
The forest plot shows the estimated standardised mean difference (SMD) with 95% confidence intervals per study. SMD > 0 means the protein has higher levels in COVID-19 patients compared to PCR-negative controls. SMD < 0 means the protein is lower in COVID-19 patients. If the 95% confidence intervals of the summary estimate overlap 0, the estimate is not statistically significant. The summary estimate includes all the studies. Please note that some proteins have outliers, which could be due to measurement errors or due to different cases and controls used in the corresponding study. Removing outliers might provide a better summary estimate of SMD.
The summary estimate is calculated with a random effects model. The prediction interval indicates the interval in which effect sizes of future studies are expected. Data on all proteins is available, regardless of whether statistically significant or not.
The funnel plotted with dashed lines plots the SMD versus the standard error (SE). Studies closer to 0 on the y axis have lower variance in their estimates and higher precision. The vertical line shows the summary effect size from the meta-analysis. When there is less publication bias, the estimates are more evenly distributed in the funnel. In the case of more publication bias, there is an assymetry in the distribution of the estimates. The contour-enhanced funnel plot demonstrates the areas where missing studies are expected due to publication bias. If the study estimates are missing in the coloured area, the assymetry is more likely due to different sources of bias apart from publication bias. If the study estimates are missing mostly in the non-coloured (white) area, the missingness is more likely due to publication bias.
The table summarises the mean and standard deviation (SD) per each study on a log2 scale, for cases and controls. Only studies that had at least three measurements in both cases and controls were included in the meta-analysis. Due to the different methodology of measurement and processing of the raw data, the values are not comparable between the studies. Because most analyses by global mass-spectrometry proteomics methods tend to produce missing values, not every protein is quantified in each sample. In the table you can find the number of cases and controls in which the protein was quantified.
Download summary table (csv)Below are plotted only the proteins that had a statistically-significant alteration in COVID-19 patients as compared to PCR-negative controls. This includes proteins identified in at least 2 studies that had an estimated standardised mean difference (SMD) and 95% CI of the SMD not overlapping 0. Proteins above and below 0 had higher and lower plasma levels in COVID-19, respectively.
There was a large heterogeneity in the SMD estimates across the studies. However, due to the nature of the random-effects-model meta-analysis on SMD estimates, the heterogeneity tends to correlate with the number of studies included. P values for the I² can be found under the Protein SMD Tab.
The summary receiver operating characteristics (SROC) curve summarises the sensitivity and specificity estimates obtained in different studies. It is calculated according to the bivariate model of Reitsma J et al. (2005. Journal of Clinical Epidemiology). using a generalised linear mixed model. The Meta-Analysis (MA) Estimate point presents the mean true positive rate (TPR, sensitivity) and mean false positive rate (FPR, 1-Specificity). The positive test was determined based on estimating an optimal cut-off point per protein in each study, to determine the optimal trade-off between sensitivity and specificity. Corresponding 95% confidence intervals (CI) of TPR and FPR and 95% CI ellipsoid area in which the expected true estimate lies are also plotted. Finally, Diagnostic odds ratios (DOR) based on Reitsma's bivariate model (with 95% CI), and a summary area under the curve (AUC) estimate is provided in the plot. As a rule of thumb, a DOR of value 10 and above and AUC of 90% and above indicate an excellent biomarker. DOR estimates with 95% confidence intervals that do not contain 1 are statistically significant. If the 95% CI and ellipsoid area are not extending beyond the Chance line, the estimates are statistically-significant and such a protein can be useful as a biomarker of COVID-19. We further estimated the preference of the study-specific ROC curves for sensitivity and specificy, following the approach by Doebbler & Holling (2015). The plot does not depict whether the protein is higher or lower in COVID-19. If you are interested in that, check under the Protein SMD tab.
The forest plots present the DOR, sensitivity and specificity estimates for selected protein. For transparency, all proteins that are identified in the analysis are plotted, regardless of whether sigificant or not. Priority should be given to the estimates of SROC curves, DOR, sensitivity and specificity provided by the bivariate Reitsma model. Please note that estimates for proteins identified in fewer studies are less reliable. Estimates based on only two studies should be considered unreliable.
Sensitivity is the proportion of true positive tests among those who had the disease - COVID-19 in this instance. The graph below contains all the sensitivity estimates per study, with 95% CI. The summary estimate is based on the bivariate Reitsma model. High sensitivity (>0.8) implies that the protein can be a useful diagnostic biomarker of COVID-19 patients, meaning that most COVID-19 patients should have altered levels of this protein.
Specificity is the proportion of true negative tests among those who did not have the disease - PCR-negative individuals in this instance. The graph below contains all the specificity estimates per study, with 95% CI. The summary estimate is based on the bivariate Reitsma model. High specificity (>0.8) implies that the protein can be a useful biomarker for excluding a diagnosis of COVID-19.
The Diagnostic odds ratio (DOR) represent the ratio between the odds od testing positive in individuals with COVID-19 and the odds of testing negative in individuals who were PCR-negative in the corresponding studies. The summary DOR in this plot was estimated with a univariate method (Glas et al. Journal of Clinical Epidemiology). Proteins with 95% CI of log(DOR) that do not overlap 0 are statistically significant. As a rule of thumb, a DOR of value 10 and above (corresponding to a log value of 2.23 in the plot) indicates a very good biomarker.
Below are plotted only the proteins that had a statistically significant SROC based on the bivariate model, i.e., proteins that had lower 95% CI limit of Sensitivity and Specificity > 0.5. All proteins that have been analysed are plotted. However, we advise not to rely upon estimates derived only from two studies. In general, the more studies included in the estimate, the more confident is the SROC curve. Please note that protein estimates derived from a different number of studies are not comparable.
The 3D plot allows comparison between the proteins by showing the Area Under the Curve (AUC) on the x axis, mean Specificity on the y axis and mean Sensitivity on the z axis. Proteins that had the highest values of all three parameters might be most useful as diagnostic biomarkers. Again, estimates derived only from fewer studies should be interpreted with caution.
CoViMAPP was developed with the work of a multidisciplinary group of researchers from the Karolinska Institute (Stockholm, Sweden), led by Dr. Jonas Klingström and Dr. Maria Pernemalm. This resource was developed as part of the project on in-depth plasma proteomics in COVID-19, where the authors have performed in-depth plasma proteome profiling in COVID-19, traced the signal to SARS-CoV-2 infection in vitro, validated previous findings and reported new alterations of the plasma proteome in COVID-19 patients. Details about this work can be found in the manuscript: Comprehensive proteomics and meta-analysis of COVID-19 host response by Babačić et al. (2023). If using CoViMAPP, please cite this manuscript.
PRISMA systematic review flow chart. In order to identify studies that performed global mass-spectrometry proteomics analysis of COVID-19 samples and PCR-negative controls, we performed a systematic review by searching two reference databases. The chart shows the number of references identified, screened, and excluded from the review.
